Personal Health Record and AI for Precision Medicine

Contents
1. Why Personal Health Record and Artificial
Intelligence ?
2. Obesity Example
3. Personal Health Record
① Genetic Data
② Electrical Health Records
③ National Healthcare Data
④ Medical Images
⑤ Sensor/Mobile Data
⑥ Data Integration
4. PHR+AI Applications
2

Contents
Intelligence ?
2. Obesity Example
① Genetic Data
④ Medical Images
3

Why PHR and AI?
4
Healthcare Big Data Machine Learning
Novel Insights and Applications

$215MPrecision Medicine Initiative
2015/1/30

Tipping Point for Big Data Healthcare
2013 McKinsey The big data revolution in healthcare

Hypothesis Driven Science Data Driven Science
Hypothesis
Collect
Data
Data
Generate
Hypothesis
Analyze
Analyze

Candidate Gene
Approach
Genome-wide
Approach
Choose a Gene
from Prior Knowledge
Analyze the Gene
Analyze ALL Genes
Discover Novel Findings

GWAS
(Genome wide association study)
SNP chip
Whole Genome
SNP Profiling (500K~1M SNPs)
Common Variant
Choi HJ, Doctoral Thesis

Estrada et al., Nature Genetics, 2012
+ novel targets
for bone biology
Recent largest GWAS
GEFOS consortium

2010 An Environment-Wide
Association Study (EWAS) on Type
2 Diabetes Mellitus
Environment-Wide Association Study (EWAS)
 다양한 환경인자들 

GWAS  PheWAS
Phenotype-wide Association Study

1000 개의 질병들
Bioinformatics. 2010 PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations.
Phenotype-wide Association Study

Genome-Envirome-Phenome-wide
Association Study
Phenome-wide
(Lab,Diagnosis)
Proposal
(Choi)
Genome-wide
Environment-wide
(Life style, diet, exercise, pollution)

Anatome-Phenome-wide
Association Study
2015.2.19. Nature. Genetic and epigenetic fine mapping of causal autoimmune disease variants
Phenome
Anatome

Machine Learning
2014 Big data bioinformatics

의료 빅데이터의 새로운 역할
전통적인 관점 연구
Large scale
(unstructured)
data
Summary
(Modify)
Classical hypothesis driven study
새로운 관점 연구
Hypothesis Generating Study

Contents
Intelligence ?
2. Obesity Example
① Genetic Data
④ Medical Images
21

22
Environment
Gene
Eat
Exercise
Metabolism
Brain
Glucose
DM
Blood Pressure
HTN
Cardiovascular
Disease
Cognitive
Hormone
Behavior
Psychotherapy
Behavior Therapy
Policy Making
Genetic Testing
Neuroimaging
Neuromodulation
Drug
Drug
Lab
Survey
Survey
Sensor
DrugDiagnosis
EMR
Government
Data
Mining

Environment Survey
NeuroimagingGenetics Lab/Hormone Hospital
Cognitive
Personalize
Psychotherapy
Dietary
Intervention
Exercise
Intervention
Food
Exercise
Glucose
Mobile
Drug
Neuromodulation
Monitoring

Contents
Intelligence ?
2. Obesity Example
① Genetic Data
④ Medical Images
24

Disease genetic susceptibility
Cancer driver
somatic mutation
Pharmacogenomics
Targeted
Cancer Treatment
(EGFR)
Causal
Variant
Targeted Drug
(MODY-SU)
Drug Efficacy/Side Effect
Related Genotype
(CYP, HLA)
Genetic Diagnosis
(Mendelian,
Cystic fibrosis)
Molecular
Classification
- Prognosis
(Leukemia)
Hereditary
Cancer
(BRCA)
Microbiome
(Bacteria,
Virus)
Genomic Medicine
Risk prediction
(Complex disease,
Diabetes)
Germline Variants

2014 NEJM Genotype–Phenotype Correlation — Promiscuity in the Era of Next-Generation Sequencing

Tissue Specific Expression
Comprehensive Catalogues of Genomic Data
Variation in the human genome
Mendelian (monogenic) diseases
(N=22,432)
Whole genome sequencing (N=1,000)
Four ethnic groups
(CEU, YRI, JPT, CHB, N=270)
GWAS catalog
Complex (multigenic) traits
(1926 publications and 13410 SNPs)
Disease-related variations
Functional elements
2014-06-29
27

저의 유전자
분석 결과를
반영하여 진료
해주세요!! 헠?
28

(출처: 금창원 대표님 블로그)
$99
TV CF
32

Pharmacogenetic Tests: 최형진
No
Drug
(N= 10)
Gene
(6 genes=8 bioma
rkers)
Target SNPs
(N=12)
#5
(HJC)
Genotype Interpretation Clinical Interpretation
1 Clopidogrel CYP2C19
rs4244285 (G>A) GG
*1/*1
(EM)
Use standard dosers4986893 (G>A) GG
rs12248560 (C>T) CC
2 Warfarin
VKORC1 rs9923231 (C>T) TT
Low dose
(higher risk of bleeding)
Warfarin dose=0.5~2 mg/day
CYP2C9
rs1799853 (C>T) CC
rs1057910 (A>C) AC
3 Simvastatin SLCO1B1 rs4149056 (T>C) TT Normal
4
Azathioprine (AP),
MP, or TG
TPMT rs1142345 (A>G) AA Normal
5
Carbamazepine
or Phenytoin
HLA-B*1502
rs2844682 (C>T) CT
Normal
rs3909184 (C>G) CC
6 Abacavir HLA-B*5701 rs2395029 (T>G) TT Normal
7 Allopurinol HLA-B*5801 rs9263726 (G>A) GG Normal
Clopidogrel1)
: UM/EM=standard dose, IM/PM= consider alternative antiplatelet agent (eg. prasugrel/ticagrelor)
Warfarin2)
: high dose=5~7 mg/day, medium dose=3~4 mg/day, low dose=0.5~2 mg/day
=0
최형진
+1,000,000
?

Future of Genomic Medicine?
Test when neededWithout information Know your type
Blood
type
Geno
type
Here is my
sequence

Genetics of eating behavior
2011 Genetics of eating behavior

Gene-Environment Interaction
Gene Environment
Disease
Genetic Predisposition Score Sugar-Sweetened Beverages

Soda School
No-Soda School
Obese Family Lean Family

Contents
Intelligence ?
2. Obesity Example
① Genetic Data
④ Medical Images
43

Electronic Health Records
2012 NRG Mining electronic health records- towards better research applications and clinical care
44

PCA Analysis혈당
신장기능
45

Machine Learning
2014 Big data bioinformatics46

밤동안 저혈당수면 Lt.foot rolling Keep떨림,
식은땀, 현기증, 공복감, 두통, 피로감등의 저혈
당 에 저혈당 이 있을 즉알려주도록 밤사이 특
이호소 수면유지상처와 통증 상처부위 출혈
oozing, severe pain 알리도록 고혈당 처방된 당
뇨식이의 중요성과 간식을 자제하도록 .고혈
당 ,,관리 방법 .당뇨약 이해 잘 하고 수술부위
oozing Rt.foot rolling keep드레싱 상태를 고혈
당 고혈당 의식변화 BST 387 checked.고혈당
으로 인한 구강 내 감염 위해 식후 양치, gargle
등 구강 위생 격려.당뇨환자의 발관리 방법에 .
목표 혈당, 목표 당화혈색소에 .식사를 거르거
나 지연하지 않도록 .식사요법, 운동요법, 약물
요법을 정확히 지키는 것이 중요을 .처방된 당
뇨식이의 중요성과 간식을 자제하도록 .고혈
당 ,,관리 방법 .혈당 정상 범위임rt foot rolling
중으로 pain호소 밤사이 수면양호걱정신경 예
민감정변화 중임감정을 표현하도록 지지하고
경청기분상태 condition 조금 나은 듯 하다고 혈
당 조절과 관련하여 신경쓰는 모습 보이며 혈당
self로 측정하는 모습 보임혈당 조절에 안내하
고 불편감 지속알리도록고혈당 고혈당 의식변
화 고혈당 허약감 지남력 혈당조절 안됨고혈당
으로 인한 구강 내 감염 위해 식후 양치, gargle
등 구강 위생 격려.당뇨환자의 정기점검 내용과
빈도에 .BST 140 으로 저혈당 호소 밤동안 저
혈당수면 Lt.foot rolling Keep떨림, 식은땀, 현
기증, 공복감, 두통, 피로감등의 저혈당 에 저
혈당 이 있을 즉알려주도록 pain 및 불편감 호
소 WA 잘고혈당 고혈당 의식변화 고혈당 허
약감 지남력 혈당조절 안됨식사요법, 운동요법,
약물요법을 정확히 지키는 것이 중요을 .저혈당
/고혈당 과 대처법에 .혈당정상화, 표준체중의
유지, 정상 혈중지질의 유지에 .고혈당 ,,관리
방법 .혈당측정법,인슐린 자가 투여법, 경구투
약,수분 섭취량,대체 탄수화물,의료진의 도움이
필요한 사항에 교혈당 정상 범위임수술부위
oozing Rt.foot rolling keep수술 부위 (출혈, 통
증, 부종)수술부위 출혈 상처부위 oozing
Wound 당겨지지 않도록 적절한 체위 취하기
설명감염 발생 위험 요인 수술부위 출혈 밤동안
간호기록지 Word Cloud
Natural Language Processing (NLP)48

Contents
Intelligence ?
2. Obesity Example
① Genetic Data
④ Medical Images
49

Big data platform model by Korea Institute of
Drug Safety and Risk Management

Contents
Intelligence ?
2. Obesity Example
① Genetic Data
④ Medical Images
55

Heart
SIMENS: CT Cardio-Vascular Engine

2013 Science Structural and Functional Brain Networks- From Connections to Cognition

Green Lines: orexigenic pathways
Red lines: dopaminergic pathways
2013 The contribution of brain reward circuits to the obesity epidemic
2014 Obesity – A neuropsychological disease- Systematic review and neuropsychological mode
A, amygdala;
H, hypothalamus;
NA, nucleus accumbens,
PFC, prefrontal cortex;
VTA, ventral tegmental area
58
tDCS Neuromodulation Controls
Feeding Behavior via
Food Reward Activity and Connectivity
Neuromodulation Brain Activity Feeding Behavior
Brain Connectivity

fMRI analysis
2013 Science Functional interactions as big data in the human brain

62
2012 Decoding subject-driven cognitive states with whole-brain connectivity patterns

2014
Radiomics
2014 Nature Communications. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

Quantitative nuclear morphometry
2015 Laboratory Investigation. Novel genotype-phenotype associations in human cancers enabled by advanced molecular platforms and computational analysis
of whole slide images

Contents
Intelligence ?
2. Obesity Example
① Genetic Data
④ Medical Images
65

66
2015 Sci Transl Med. The emerging field of mobile health

스마트폰 활용 당뇨병 통합관리
의사
상담 교육
조회/
분석
교육간호사
매주/필요시
진료 처방
2-3달 간격
평가 회의
매주/필요시
식사/
운동
스마트폰
데이터베이스 서버
자가관리
전송
분석
혈당
측정
혈당측정기

Date Time name foodType calories unit amount
2014-08-09 0 미역국 0 23 1국그릇 (300ml) 105 g
2014-08-09 0 잡곡밥 0 80 1/4공기 (52.5g) 52 g
2014-08-09 0 열무김치 0 3 1/4소접시 (8.75g) 9 g
2014-08-09 0 파프리카 0 6 1/2개 (33.25g) 35 g
2014-08-09 0 토란대무침 0 28 1/2소접시(46.5g) 46 g
2014-08-09 1 복숭아 0 91 1개 (269g) 268 g
2014-08-09 2 마른오징어 2 88 1/4마리 (25g) 25 g
2014-08-09 2 파프리카 0 6 1/2개 (33.25g) 35 g
2014-08-09 2 저지방우유 1 72 1컵 (200ml) 180 g
2014-08-09 2 복숭아 0 183 2개 (538g) 538 g
2014-08-09 3 복숭아 0 91 1개 (269g) 268 g
2014-08-09 3 파프리카 0 6 1/2개 (33.25g) 35 g
2014-08-09 4 파프리카 0 6 1/2개 (33.25g) 35 g
2014-08-09 4 식빵 1 92 1장 (33g) 33 g
2014-08-09 4 삶은옥수수 1 197 1개 반 (150g) 150 g
2014-08-09 4 복숭아 0 91 1개 (269g) 268 g
2014-08-09 4 저지방우유 1 72 1컵 (200ml) 180 g
2014-08-10 0 복숭아 0 91 1개 (269g) 268 g
2014-08-10 0 저지방우유 1 36 1/2컵 (100ml) 90 g
2014-08-10 0 두부 0 20 1/4인분 (25g) 25 g
2014-08-10 0 견과류 2 190 1/4 컵 (50g) 31 g
2014-08-10 0 파프리카 0 11 1개 (66.5g) 65 g
68

식사량 실시간 모니터링
0
100
200
300
400
500
600
아침
아침간식
점심
점심간식
저녁
저녁간식
아침
아침간식
점심
점심간식
저녁
저녁간식
아침
아침간식
점심
점심간식
저녁
저녁간식
아침
아침간식
점심
점심간식
저녁
저녁간식
아침
아침간식
점심
점심간식
저녁
저녁간식
아침
아침간식
점심
점심간식
저녁
저녁간식
아침
아침간식
점심
점심간식
저녁
저녁간식
2014-08-09 2014-08-10 2014-08-11 2014-08-12 2014-08-13 2014-08-14 2014-08-15
점심 과식
저녁 금식
69

운동량 실시간 모니터링
0
5000
10000
15000
20000
25000
걸음 수
운동X
70

Social Network and
Obesity Prevalence
2013 PLOS One. Assessing the Online Social Environment for Surveillance of Obesity Prevalence

2013 PLOS CB Reassessing Google Flu Trends Data for Detection of Seasonal and Pandemic Influenza
Google Flu Trends

Contents
Intelligence ?
2. Obesity Example
① Genetic Data
④ Medical Images
77

2014 JAMA Finding the Missing Link for Big Biomedical Data

83
국가기관
의료기관
연구기관
개인
개인식별
정보통합
정보저장
통계
인공지능
건강관리
정보공개
수집 분석 활용
개인
집단
Overview of Healthcare Data

2015 Nature Immunology. A vision and a prescription for big data–enabled medicine

Contents
Intelligence ?
2. Obesity Example
① Genetic Data
④ Medical Images
85

90
In a scan of 3,000 images, IBM
technology was able to spot
melanoma with an accuracy
of about 95 percent, much
better than the 75 percent to
84 percent average of today's
largely manual methods.
IBM Research will continue to
work with Sloan Kettering to
develop additional
measurements and
approaches to further refine
diagnosis, as well as refine
their approach through larger
sets of data.
Dec 17, 2014

91
Aug. 11, 2015
IBM is betting that the same technology that
recognizes cats can identify tumors and other signs of
diseases.
In the long run, IBM and others in the field hope such
systems can become reliable advisers to
radiologists, dermatologists and other practitioners
who analyze images—especially in parts of the world
where health-care providers are scarce.
While IBM hopes Watson will learn to interpret
Merge’s images, it also expects the combination of
imagery, medical records and other data to reveal
patterns relevant to diagnosis and treatment that a
human physician may miss, ushering in an era of
computer-assisted care. Two other recent IBM
acquisitions, Phytel Inc. and Explorys Inc., yielded 50
million electronic medical records.

The FUTURE MEDICINE
is already
at PRESENT
94

Personal Health Record and AI for Precision Medicine

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